In [49]:
from pyecharts.faker import Faker
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.globals import ChartType, SymbolType
In [50]:
import pandas as pd
In [51]:
df=pd.read_csv("/Users/Lenovo/Desktop/data/most population.csv",encoding='gbk')
In [52]:
df
Out[52]:
In [53]:
country=list(df.Country)
In [54]:
year=list(df['2018'])
In [55]:
各国人口密度=list(zip(country,year))
In [56]:
各国人口密度
Out[56]:
In [60]:
def map_world() -> Map:
c = (
Map()
.add("各国人口密度",各国人口密度, "world")
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="2018年"),
visualmap_opts=opts.VisualMapOpts(max_=600),
)
)
return c
In [61]:
ditu=map_world()
ditu.render_notebook()
Out[61]:
In [62]:
def map_world() -> Map:
c = (
Map()
.add("各国人口密度",各国人口密度, "world")
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="1968年"),
visualmap_opts=opts.VisualMapOpts(max_=600),
)
)
return c
In [63]:
ditu=map_world()
ditu.render_notebook()
Out[63]:
本项目通过分析世界各个国家还有每个国家下属城市的人口密度，以此来对一些人口分布不均的地区提出一些有效的建议来解决问题¶
结论：¶
上面的地图对比了1968到2018这五十年来人口密度的变化
从两张地图的颜色对比可以明显看出，世界人口密度最高的区域集中在亚洲，例如中国，印度，日本，新加坡等国家，这50年的时间里，欧洲、南北美洲、澳洲、非洲的人口增长都相对比较缓慢，人口密度不算高。
建议：¶
- 为了让人口在全球内得到合理的分布，可以通过借助经济、 政治、文化力量， 在经济上， 借助跨国公司等渠道促进人口合理分布；在政治上可形成联盟，加强成员国之间的沟通，达到互助互利；在文化上可以通过举办世界旅游节等活动，让更多的人知道各国的魅力所在，吸引人口迁移。
In [1]:
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib.animation as animation
from IPython.display import HTML
In [2]:
df = pd.read_csv('https://gist.githubusercontent.com/johnburnmurdoch/4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations',
usecols=['name', 'group', 'year', 'value'])
df.head(5)
Out[2]:
In [3]:
current_year = 2018
dff = (df[df['year'].eq(current_year)]
.sort_values(by='value', ascending=True)
.head(10))
dff
Out[3]:
In [4]:
colors = dict(zip(
['India', 'Europe', 'Asia', 'Latin America',
'Middle East', 'North America', 'Africa'],
['#adb0ff', '#ffb3ff', '#90d595', '#e48381',
'#aafbff', '#f7bb5f', '#eafb50']
))
group_lk = df.set_index('name')['group'].to_dict()
In [6]:
fig, ax = plt.subplots(figsize=(15, 8))
dff = dff[::-1] # 从上到下翻转值
# 将颜色值传递给`color=`
ax.barh(dff['name'], dff['value'], color=[colors[group_lk[x]] for x in dff['name']])
# 遍历这些值来绘制标签和值(Tokyo, Asia, 38194.2)
for i, (value, name) in enumerate(zip(dff['value'], dff['name'])):
ax.text(value, i, name, ha='right') # Tokyo: 名字
ax.text(value, i-.25, group_lk[name], ha='right') # Asia: 组名
ax.text(value, i, value, ha='left') # 38194.2: 值
# 在画布右方添加年份
ax.text(1, 0.4, current_year, transform=ax.transAxes, size=46, ha='right')
Out[6]:
In [8]:
fig, ax = plt.subplots(figsize=(15, 8))
def draw_barchart(year):
dff = df[df['year'].eq(year)].sort_values(by='value', ascending=True).tail(10)
ax.clear()
ax.barh(dff['name'], dff['value'], color=[colors[group_lk[x]] for x in dff['name']])
dx = dff['value'].max() / 200
for i, (value, name) in enumerate(zip(dff['value'], dff['name'])):
ax.text(value-dx, i, name, size=14, weight=600, ha='right', va='bottom')
ax.text(value-dx, i-.25, group_lk[name], size=10, color='#444444', ha='right', va='baseline')
ax.text(value+dx, i, f'{value:,.0f}', size=14, ha='left', va='center')
# ... polished styles
ax.text(1, 0.4, year, transform=ax.transAxes, color='#777777', size=46, ha='right', weight=800)
ax.text(0, 1.06, 'Population (thousands)', transform=ax.transAxes, size=12, color='#777777')
ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:,.0f}'))
ax.xaxis.set_ticks_position('top')
ax.tick_params(axis='x', colors='#777777', labelsize=12)
ax.set_yticks([])
ax.margins(0, 0.01)
ax.grid(which='major', axis='x', linestyle='-')
ax.set_axisbelow(True)
ax.text(0, 1.12, 'The most populous cities in the world from 1500 to 2018',
transform=ax.transAxes, size=24, weight=600, ha='left')
# ax.text(1, 0, 'by QIML', transform=ax.transAxes, ha='right',
# color='#777777', bbox=dict(facecolor='white', alpha=0.8, edgecolor='white'))
plt.box(False)
draw_barchart(2018)
import matplotlib.animation as animation
from IPython.display import HTML
fig, ax = plt.subplots(figsize=(15, 8))
animator = animation.FuncAnimation(fig, draw_barchart, frames=range(1968, 2019))
HTML(animator.to_jshtml())
# or use animator.to_html5_video() or animator.save()
Out[8]:
